Python machine learning library based on Object Oriented design principles; the goal is to allow users to quickly explore data and search for top machine learning algorithm candidates for a given dataset
MIT License
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implement “one-standard-error” rule for ModelTuner #30
e.g. add a property that gives a list of all models (i.e. hyper-parameters) within 1 standard deviation of best hyper-parameter combination. The intent is to choose the model that has the lowest-flexibility/highest-bias (i.e. simplest model) to reduce chances of overfitting on unseen data.
e.g. add a property that gives a list of all models (i.e. hyper-parameters) within 1 standard deviation of best hyper-parameter combination. The intent is to choose the model that has the lowest-flexibility/highest-bias (i.e. simplest model) to reduce chances of overfitting on unseen data.